Apache Beam turns ten. From Google's 2015 Dataflow paper to 4 trillion daily events at LinkedIn — what it got right, where it falls short, and what comes next.
Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.
Classify requests (dashboards vs exploration/jobs), cap and prioritize concurrency, and fall back to cache/rollups so critical dashboards stay responsive during spikes.
CI/CD-driven modernization of data platforms, improving release speed, observability, and reliability through automation, parallelization, and job-level telemetry.
Learn to transform Spring Boot REST APIs into an event-driven architecture by utilizing Kafka, RabbitMQ, or NATS to enhance scalability, resilience, and responsiveness.
Retry transient failures, route poison messages to a DLQ, and deduplicate with a DB table three layers that turn a fragile Kafka consumer into a fault tolerant one.
Platform turning complex smart meter data into usable, real-time insights via APIs — enabling scalable analytics, efficiency, and smarter energy decisions.
Learn how agentic data pipelines go beyond big data to power modern AI workloads with autonomous decision-making, real-time adaptability, and intelligent data.
Modern Java backend design is evolving from traditional APIs to event-driven architectures, enabling more scalable, resilient, and real-time distributed systems.
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